Lu Nan, Zhang Huaqiang, Dong Chunmei, Li Hongtao, Chen Yu
Department of Instrumentation, School of Mechanical Engineering, West Campus, Shandong University of Technology, Zibo 255000, China.
Beijing Institute of Space Launch Technology, Beijing 100076, China.
Micromachines (Basel). 2025 Jan 10;16(1):73. doi: 10.3390/mi16010073.
When using a fiber optic gyroscope as the core measurement element in an inertial navigation system, its work stability and reliability directly affect the accuracy of the navigation system. The modeling and fault diagnosis of the gyroscope is of great significance in ensuring the high accuracy and long endurance of the inertial system. Traditional diagnostic models often encounter challenges in terms of reliability and accuracy, for example, difficulties in feature extraction, high computational cost, and long training time. To address these challenges, this paper proposes a new fault diagnostic model that performs a fault diagnosis of gyroscopes using the enhanced capsule neural network (iCaps NN) optimized by the improved gray wolf algorithm (NIGWO). The wavelet packet transform (WPT) is used to construct a two-dimensional feature vector matrix, and the deep feature extraction module (DFE) is added to extract deep-level information to maximize the fault features. Then, an improved gray wolf algorithm combined with the adaptive algorithm (Adam) is proposed to determine the optimal values of the model parameters, which improves the optimization performance. The dynamic routing mechanism is utilized to greatly reduce the model training time. In this paper, effectiveness experiments were carried out on the simulation dataset and real dataset, respectively; the diagnostic accuracy of the fault diagnosis method in this paper reached 99.41% on the simulation dataset; the loss value in the real dataset converged to 0.005 with the increase in the number of iterations; and the average diagnostic accuracy converged to 95.42%. The results show that the diagnostic accuracy of the NIGWO-iCaps NN model proposed in this paper is improved by 13.51% compared with the traditional diagnostic methods. It effectively confirms that the method in this paper is capable of efficient and accurate fault diagnosis of FOG and has strong generalization ability.
当在惯性导航系统中使用光纤陀螺仪作为核心测量元件时，其工作稳定性和可靠性直接影响导航系统的精度。陀螺仪的建模与故障诊断对于确保惯性系统的高精度和长续航能力具有重要意义。传统诊断模型在可靠性和准确性方面常常面临挑战，例如，特征提取困难、计算成本高以及训练时间长。为应对这些挑战，本文提出一种新的故障诊断模型，该模型使用经改进灰狼算法（NIGWO）优化的增强胶囊神经网络（iCaps NN）对陀螺仪进行故障诊断。利用小波包变换（WPT）构建二维特征向量矩阵，并添加深度特征提取模块（DFE）来提取深层次信息，以最大化故障特征。然后，提出一种结合自适应算法（Adam）的改进灰狼算法来确定模型参数的最优值，从而提高优化性能。利用动态路由机制大幅减少模型训练时间。本文分别在仿真数据集和真实数据集上进行了有效性实验；本文提出的故障诊断方法在仿真数据集上的诊断准确率达到99.41%；在真实数据集中，损失值随着迭代次数的增加收敛到0.005；平均诊断准确率收敛到95.42%。结果表明，本文提出的NIGWO-iCaps NN模型的诊断准确率相比传统诊断方法提高了13.51%。有效证实了本文方法能够对光纤陀螺仪进行高效、准确的故障诊断，且具有较强的泛化能力。
